


import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np
class 					SCREAMING_SNAKE_CASE__    :
      def 			SCREAMING_SNAKE_CASE				(			self							:			Optional[Any]	)						->     List[str]:
             torch.manual_seed(0	)
             a_   :						Any				=    TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'	)

             torch.manual_seed(0	)
             a_   :						Optional[Any]				=    AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'	)

             torch.manual_seed(0	)
             a_   :						str				=    UNetaDConditionModel(
                 sample_size=3_2	,				layers_per_block=1	,				block_out_channels=[3_2, 6_4]	,				down_block_types=[
                     'ResnetDownsampleBlock2D',
                     'SimpleCrossAttnDownBlock2D',
                 ]	,				mid_block_type='UNetMidBlock2DSimpleCrossAttn'	,				up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']	,				in_channels=3	,				out_channels=6	,				cross_attention_dim=3_2	,				encoder_hid_dim=3_2	,				attention_head_dim=8	,				addition_embed_type='text'	,				addition_embed_type_num_heads=2	,				cross_attention_norm='group_norm'	,				resnet_time_scale_shift='scale_shift'	,				act_fn='gelu'	,				)
             unet.set_attn_processor(AttnAddedKVProcessor()	)  # For reproducibility tests

             torch.manual_seed(0	)
             a_   :						Tuple				=    DDPMScheduler(
                 num_train_timesteps=1_0_0_0	,				beta_schedule='squaredcos_cap_v2'	,				beta_start=0.0001	,				beta_end=0.02	,				thresholding=UpperCAmelCase_	,				dynamic_thresholding_ratio=0.95	,				sample_max_value=1.0	,				prediction_type='epsilon'	,				variance_type='learned_range'	,				)

             torch.manual_seed(0	)
             a_   :						Union[str, Any]				=    IFWatermarker()

             return {
                 "text_encoder": text_encoder,
                 "tokenizer": tokenizer,
                 "unet": unet,
                 "scheduler": scheduler,
                 "watermarker": watermarker,
                 "safety_checker": None,
                 "feature_extractor": None,
             }
      def 			SCREAMING_SNAKE_CASE				(			self							:			Tuple	)						->     List[str]:
             torch.manual_seed(0	)
             a_   :						int				=    TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'	)

             torch.manual_seed(0	)
             a_   :						Optional[Any]				=    AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'	)

             torch.manual_seed(0	)
             a_   :						Any				=    UNetaDConditionModel(
                 sample_size=3_2	,				layers_per_block=[1, 2]	,				block_out_channels=[3_2, 6_4]	,				down_block_types=[
                     'ResnetDownsampleBlock2D',
                     'SimpleCrossAttnDownBlock2D',
                 ]	,				mid_block_type='UNetMidBlock2DSimpleCrossAttn'	,				up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']	,				in_channels=6	,				out_channels=6	,				cross_attention_dim=3_2	,				encoder_hid_dim=3_2	,				attention_head_dim=8	,				addition_embed_type='text'	,				addition_embed_type_num_heads=2	,				cross_attention_norm='group_norm'	,				resnet_time_scale_shift='scale_shift'	,				act_fn='gelu'	,				class_embed_type='timestep'	,				mid_block_scale_factor=1.414	,				time_embedding_act_fn='gelu'	,				time_embedding_dim=3_2	,				)
             unet.set_attn_processor(AttnAddedKVProcessor()	)  # For reproducibility tests

             torch.manual_seed(0	)
             a_   :						List[str]				=    DDPMScheduler(
                 num_train_timesteps=1_0_0_0	,				beta_schedule='squaredcos_cap_v2'	,				beta_start=0.0001	,				beta_end=0.02	,				thresholding=UpperCAmelCase_	,				dynamic_thresholding_ratio=0.95	,				sample_max_value=1.0	,				prediction_type='epsilon'	,				variance_type='learned_range'	,				)

             torch.manual_seed(0	)
             a_   :						Optional[Any]				=    DDPMScheduler(
                 num_train_timesteps=1_0_0_0	,				beta_schedule='squaredcos_cap_v2'	,				beta_start=0.0001	,				beta_end=0.02	,				)

             torch.manual_seed(0	)
             a_   :						Any				=    IFWatermarker()

             return {
                 "text_encoder": text_encoder,
                 "tokenizer": tokenizer,
                 "unet": unet,
                 "scheduler": scheduler,
                 "image_noising_scheduler": image_noising_scheduler,
                 "watermarker": watermarker,
                 "safety_checker": None,
                 "feature_extractor": None,
             }
      def 			SCREAMING_SNAKE_CASE				(			self							:			Tuple	)						->     List[str]:
             a_   :						Tuple				=    self.get_dummy_components()
             a_   :						Any				=    self.pipeline_class(**UpperCAmelCase_	)
             pipe.to(UpperCAmelCase_	)
             pipe.set_progress_bar_config(disable=UpperCAmelCase_	)

             a_   :						int				=    self.get_dummy_inputs(UpperCAmelCase_	)

             a_   :						Optional[int]				=    inputs['prompt']
             a_   :						Any				=    inputs['generator']
             a_   :						List[str]				=    inputs['num_inference_steps']
             a_   :						Dict				=    inputs['output_type']

             if "image" in inputs:
                    a_   :						List[str]				=    inputs['image']
             else:
                    a_   :						Any				=    None

             if "mask_image" in inputs:
                    a_   :						Dict				=    inputs['mask_image']
             else:
                    a_   :						Dict				=    None

             if "original_image" in inputs:
                    a_   :						str				=    inputs['original_image']
             else:
                    a_   :						Optional[Any]				=    None

             a_				,						a_   :						Optional[Any]				=    pipe.encode_prompt(UpperCAmelCase_	)

             # inputs with prompt converted to embeddings
             a_   :						Any				=    {
                 'prompt_embeds': prompt_embeds,
                 'negative_prompt_embeds': negative_prompt_embeds,
                 'generator': generator,
                 'num_inference_steps': num_inference_steps,
                 'output_type': output_type,
             }

             if image is not None:
                    a_   :						List[str]				=    image

             if mask_image is not None:
                    a_   :						Union[str, Any]				=    mask_image

             if original_image is not None:
                    a_   :						str				=    original_image

             # set all optional components to None
             for optional_component in pipe._optional_components:
                    setattr(UpperCAmelCase_	,				UpperCAmelCase_	,				UpperCAmelCase_	)

             a_   :						Tuple				=    pipe(**UpperCAmelCase_	)[0]

             with tempfile.TemporaryDirectory() as tmpdir:
                    pipe.save_pretrained(UpperCAmelCase_	)
                    a_   :						int				=    self.pipeline_class.from_pretrained(UpperCAmelCase_	)
                    pipe_loaded.to(UpperCAmelCase_	)
                    pipe_loaded.set_progress_bar_config(disable=UpperCAmelCase_	)

             pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()	)  # For reproducibility tests

             for optional_component in pipe._optional_components:
                    self.assertTrue(
                        getattr(UpperCAmelCase_	,				UpperCAmelCase_	) is None	,				F"""`{optional_component}` did not stay set to None after loading."""	,				)

             a_   :						str				=    self.get_dummy_inputs(UpperCAmelCase_	)

             a_   :						Union[str, Any]				=    inputs['generator']
             a_   :						Optional[Any]				=    inputs['num_inference_steps']
             a_   :						Union[str, Any]				=    inputs['output_type']

             # inputs with prompt converted to embeddings
             a_   :						List[str]				=    {
                 'prompt_embeds': prompt_embeds,
                 'negative_prompt_embeds': negative_prompt_embeds,
                 'generator': generator,
                 'num_inference_steps': num_inference_steps,
                 'output_type': output_type,
             }

             if image is not None:
                    a_   :						List[Any]				=    image

             if mask_image is not None:
                    a_   :						List[Any]				=    mask_image

             if original_image is not None:
                    a_   :						Union[str, Any]				=    original_image

             a_   :						Union[str, Any]				=    pipe_loaded(**UpperCAmelCase_	)[0]

             a_   :						List[str]				=    np.abs(to_np(UpperCAmelCase_	) - to_np(UpperCAmelCase_	)	).max()
             self.assertLess(UpperCAmelCase_	,				1E-4	)

      def 			SCREAMING_SNAKE_CASE				(			self							:			List[str]	)						->     List[Any]:
             a_   :						List[Any]				=    self.get_dummy_components()
             a_   :						str				=    self.pipeline_class(**UpperCAmelCase_	)
             pipe.to(UpperCAmelCase_	)
             pipe.set_progress_bar_config(disable=UpperCAmelCase_	)

             a_   :						Optional[Any]				=    self.get_dummy_inputs(UpperCAmelCase_	)
             a_   :						List[str]				=    pipe(**UpperCAmelCase_	)[0]

             with tempfile.TemporaryDirectory() as tmpdir:
                    pipe.save_pretrained(UpperCAmelCase_	)
                    a_   :						Union[str, Any]				=    self.pipeline_class.from_pretrained(UpperCAmelCase_	)
                    pipe_loaded.to(UpperCAmelCase_	)
                    pipe_loaded.set_progress_bar_config(disable=UpperCAmelCase_	)

             pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()	)  # For reproducibility tests

             a_   :						int				=    self.get_dummy_inputs(UpperCAmelCase_	)
             a_   :						Optional[int]				=    pipe_loaded(**UpperCAmelCase_	)[0]

             a_   :						int				=    np.abs(to_np(UpperCAmelCase_	) - to_np(UpperCAmelCase_	)	).max()
             self.assertLess(UpperCAmelCase_	,				1E-4	)




"""simple docstring"""


import inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
SCREAMING_SNAKE_CASE			  =  """src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
SCREAMING_SNAKE_CASE			  =  direct_transformers_import(PATH_TO_TRANSFORMERS)

SCREAMING_SNAKE_CASE			  =  transformers.models.auto.configuration_auto.CONFIG_MAPPING

SCREAMING_SNAKE_CASE			  =  {
    # used to compute the property `self.chunk_length`
    """EncodecConfig""": ["""overlap"""],
    # used as `self.bert_model = BertModel(config, ...)`
    """DPRConfig""": True,
    # not used in modeling files, but it's an important information
    """FSMTConfig""": ["""langs"""],
    # used internally in the configuration class file
    """GPTNeoConfig""": ["""attention_types"""],
    # used internally in the configuration class file
    """EsmConfig""": ["""is_folding_model"""],
    # used during training (despite we don't have training script for these models yet)
    """Mask2FormerConfig""": ["""ignore_value"""],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    """OneFormerConfig""": ["""ignore_value""", """norm"""],
    # used during preprocessing and collation, see `collating_graphormer.py`
    """GraphormerConfig""": ["""spatial_pos_max"""],
    # used internally in the configuration class file
    """T5Config""": ["""feed_forward_proj"""],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    """MT5Config""": ["""feed_forward_proj""", """tokenizer_class"""],
    """UMT5Config""": ["""feed_forward_proj""", """tokenizer_class"""],
    # used internally in the configuration class file
    """LongT5Config""": ["""feed_forward_proj"""],
    # used internally in the configuration class file
    """SwitchTransformersConfig""": ["""feed_forward_proj"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """BioGptConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """GLPNConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """SegformerConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """CvtConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """PerceiverConfig""": ["""layer_norm_eps"""],
    # used internally to calculate the feature size
    """InformerConfig""": ["""num_static_real_features""", """num_time_features"""],
    # used internally to calculate the feature size
    """TimeSeriesTransformerConfig""": ["""num_static_real_features""", """num_time_features"""],
    # used internally to calculate the feature size
    """AutoformerConfig""": ["""num_static_real_features""", """num_time_features"""],
    # used internally to calculate `mlp_dim`
    """SamVisionConfig""": ["""mlp_ratio"""],
    # For (head) training, but so far not implemented
    """ClapAudioConfig""": ["""num_classes"""],
    # Not used, but providing useful information to users
    """SpeechT5HifiGanConfig""": ["""sampling_rate"""],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        """CLIPSegConfig""": True,
        """DeformableDetrConfig""": True,
        """DetaConfig""": True,
        """DinatConfig""": True,
        """DonutSwinConfig""": True,
        """EfficientFormerConfig""": True,
        """FSMTConfig""": True,
        """JukeboxConfig""": True,
        """LayoutLMv2Config""": True,
        """MaskFormerSwinConfig""": True,
        """MT5Config""": True,
        """NatConfig""": True,
        """OneFormerConfig""": True,
        """PerceiverConfig""": True,
        """RagConfig""": True,
        """SpeechT5Config""": True,
        """SwinConfig""": True,
        """Swin2SRConfig""": True,
        """Swinv2Config""": True,
        """SwitchTransformersConfig""": True,
        """TableTransformerConfig""": True,
        """TapasConfig""": True,
        """TransfoXLConfig""": True,
        """UniSpeechConfig""": True,
        """UniSpeechSatConfig""": True,
        """WavLMConfig""": True,
        """WhisperConfig""": True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        """JukeboxPriorConfig""": True,
        # TODO: @Younes (for `is_decoder`)
        """Pix2StructTextConfig""": True,
    }
)



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				Optional[Any]:







       """simple docstring"""
       UpperCamelCase       			=		False
       for attribute in attributes:
              for modeling_source in source_strings:
                     # check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
                     if (
                         F"config.{attribute}" in modeling_source
                         or F"getattr(config, \"{attribute}\"" in modeling_source
                         or F"getattr(self.config, \"{attribute}\"" in modeling_source
                     ):
                            UpperCamelCase       			=		True
                     # Deal with multi-line cases
                     elif (
                         re.search(
                             RF"getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*\"{attribute}\""		,   UpperCAmelCase_		,   )
                         is not None
                     ):
                            UpperCamelCase       			=		True
                     # `SequenceSummary` is called with `SequenceSummary(config)`
                     elif attribute in [
                         "summary_type",
                         "summary_use_proj",
                         "summary_activation",
                         "summary_last_dropout",
                         "summary_proj_to_labels",
                         "summary_first_dropout",
                     ]:
                            if "SequenceSummary" in modeling_source:
                                   UpperCamelCase       			=		True
                     if attribute_used:
                            break
              if attribute_used:
                     break

    # common and important attributes, even if they do not always appear in the modeling files
       UpperCamelCase       			=		[
           "bos_index",
           "eos_index",
           "pad_index",
           "unk_index",
           "mask_index",
           "image_size",
           "use_cache",
           "out_features",
           "out_indices",
       ]
       UpperCamelCase       			=		["encoder_no_repeat_ngram_size"]

       # Special cases to be allowed
       UpperCamelCase       			=		True
       if not attribute_used:
              UpperCamelCase       			=		False
              for attribute in attributes:
                     # Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
                     if attribute in ["is_encoder_decoder"] and default_value is True:
                            UpperCamelCase       			=		True
                     elif attribute in ["tie_word_embeddings"] and default_value is False:
                            UpperCamelCase       			=		True

                     # Allow cases without checking the default value in the configuration class
                     elif attribute in attributes_to_allow + attributes_used_in_generation:
                            UpperCamelCase       			=		True
                     elif attribute.endswith("_token_id"					):
                            UpperCamelCase       			=		True

                     # configuration class specific cases
                     if not case_allowed:
                            UpperCamelCase       			=		SPECIAL_CASES_TO_ALLOW.get(config_class.__name__		,   []					)
                            UpperCamelCase       			=		allowed_cases is True or attribute in allowed_cases

       return attribute_used or case_allowed



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				Optional[Any]:







       """simple docstring"""
       UpperCamelCase       			=		dict(inspect.signature(config_class.__init__					).parameters					)
       UpperCamelCase       			=		[x for x in list(signature.keys()					) if x not in ["self", "kwargs"]]
       UpperCamelCase       			=		[signature[param].default for param in parameter_names]

       # If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
       # as one variant is used, the test should pass
       UpperCamelCase       			=		{}
       if len(config_class.attribute_map					) > 0:
              UpperCamelCase       			=		{v: k for k, v in config_class.attribute_map.items()}

       # Get the path to modeling source files
       UpperCamelCase       			=		inspect.getsourcefile(UpperCAmelCase_					)
       UpperCamelCase       			=		os.path.dirname(UpperCAmelCase_					)
       # Let's check against all frameworks: as long as one framework uses an attribute, we are good.
       UpperCamelCase       			=		[os.path.join(UpperCAmelCase_		,   UpperCAmelCase_					) for fn in os.listdir(UpperCAmelCase_					) if fn.startswith("modeling_"					)]

       # Get the source code strings
       UpperCamelCase       			=		[]
       for path in modeling_paths:
              if os.path.isfile(UpperCAmelCase_					):
                     with open(UpperCAmelCase_					) as fp:
                            modeling_sources.append(fp.read()					)

       UpperCamelCase       			=		[]
       for config_param, default_value in zip(UpperCAmelCase_		,   UpperCAmelCase_					):
              # `attributes` here is all the variant names for `config_param`
              UpperCamelCase       			=		[config_param]
              # some configuration classes have non-empty `attribute_map`, and both names could be used in the
              # corresponding modeling files. As long as one of them appears, it is fine.
              if config_param in reversed_attribute_map:
                     attributes.append(reversed_attribute_map[config_param]					)

              if not check_attribute_being_used(UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					):
                     unused_attributes.append(attributes[0]					)

       return sorted(UpperCAmelCase_					)



def 						lowerCamelCase__      (				)->				List[str]:







       """simple docstring"""
       UpperCamelCase       			=		{}
       for _config_class in list(CONFIG_MAPPING.values()					):
              # Skip deprecated models
              if "models.deprecated" in _config_class.__module__:
                     continue
              # Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
              UpperCamelCase       			=		[
                  cls
                  for name, cls in inspect.getmembers(
                      inspect.getmodule(_config_class					)		,   lambda UpperCAmelCase_					: inspect.isclass(UpperCAmelCase_					)
                      and issubclass(UpperCAmelCase_		,   UpperCAmelCase_					)
                      and inspect.getmodule(UpperCAmelCase_					) == inspect.getmodule(_config_class					)		,   )
              ]
              for config_class in config_classes_in_module:
                     UpperCamelCase       			=		check_config_attributes_being_used(UpperCAmelCase_					)
                     if len(UpperCAmelCase_					) > 0:
                            UpperCamelCase       			=		unused_attributes

       if len(UpperCAmelCase_					) > 0:
              UpperCamelCase       			=		"The following configuration classes contain unused attributes in the corresponding modeling files:\n"
              for name, attributes in configs_with_unused_attributes.items():
                     error += F"{name}: {attributes}\n"

              raise ValueError(UpperCAmelCase_					)


if __name__ == "__main__":
      check_config_attributes()





